Information processing apparatus and method to organize devices

ABSTRACT

[Object] To utilize various devices more effectively by organizing such devices. [Solution] Provided is an information processing apparatus including: a device log acquisition unit configured to acquire a time-series device log including information indicating a position of each of devices from each of the devices; a grouping unit configured to classify the devices into at least one group on the basis of the device logs and a preset condition of the positions; and a relation analysis unit configured to analyze a relation between the devices in each of the at least one group on the basis of the device logs.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Phase of International PatentApplication No. PCT/JP2015/057946 filed on Mar. 17, 2015, which claimspriority benefit of Japanese Patent Application No. JP 2014-132852 filedin the Japan Patent Office on Jun. 27, 2014. Each of theabove-referenced applications is hereby incorporated herein by referencein its entirety.

TECHNICAL FIELD

The present invention relates to information processing apparatuses,information processing methods, and programs.

BACKGROUND ART

Patent Literature 1 discloses a technology for suppressing calculationcost to select an item by classifying users and items based on item uselogs of the users. For example, many technologies for performingoperation of aggregation, analysis, classification, or the like on dataof a user specified by a user account, data of a log related to itemsregistered in a database, or the like have already been proposed.

CITATION LIST Patent Literature

Patent Literature 1: JP2013-164704A

SUMMARY OF INVENTION Technical Problem

In recent years, various devices have intelligent functions. Forexample, not only information processing terminals such as a personalcomputer, a smartphone, and a tablet, various devices such as homeappliances including an air conditioner, a refrigerator, and the like, acar, and a vending machine each of which includes a communicationfunction, an information processing function, a sensing function, or thelike, have become more and more popular. Such a device includes thecommunication function. However, unlike the information processingterminal, a user is not always identified by a login operation or thelike. For example, like the vending machine, some device is configuredto be used by many and unspecified users.

In a way similar to the information processing terminal, such a devicecan be used as a means for acquiring a device usage log and the like asstatistical information of a user, or a means for outputting informationto a user, for example. However, as described above, such a device doesnot identify a user (even in the case where the device is used by thespecific user), or is configured to be used by many and unspecifiedusers. Therefore, it is difficult to organize and use devices.

Accordingly, the present disclosure proposes a novel and improvedinformation processing apparatus, information processing method, andprogram that are capable of utilizing various devices more effectivelyby organizing such devices.

Solution to Problem

According to the present disclosure, there is provided an informationprocessing apparatus including: a device log acquisition unit configuredto acquire a time-series device log including information indicating aposition of each of devices from each of the devices; a grouping unitconfigured to classify the devices into at least one group on the basisof the device logs and a preset condition of the positions; and arelation analysis unit configured to analyze a relation between thedevices in each of the at least one group on the basis of the devicelogs.

According to the present disclosure, there is provided an informationprocessing method including: acquiring a time-series device logincluding information indicating a position of each of devices from eachof the devices; classifying, by a processor, the devices into at leastone group on the basis of the device logs and a preset condition of thepositions; and analyzing a relation between the devices in each of theat least one group on the basis of the device logs.

According to the present disclosure, there is provided a program forcausing a computer to achieve: a function of classifying devices into atleast one group on the basis of a preset condition of a position of eachof the devices and a time-series device log that has been acquired fromeach of the devices and that includes information indicating theposition of each of the devices; and a function of analyzing a relationbetween the devices in each of the at least one group on the basis ofthe device logs.

Advantageous Effects of Invention

As described above, according to the present disclosure, it is possibleto utilize various devices more effectively by organizing such devices.

Note that the effects described above are not necessarily limitative.With or in the place of the above effects, there may be achieved any oneof the effects described in this specification or other effects that maybe grasped from this specification.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a schematic configuration of a systemaccording to a first embodiment of the present disclosure.

FIG. 2 is a block diagram schematically illustrating a functionalconfiguration of a server according to an embodiment of the presentdisclosure.

FIG. 3 is a flowchart illustrating an example of a process performed bya correlation determination unit according to the first embodiment ofthe present disclosure.

FIG. 4A is a diagram illustrating an example of a device log DBaccording to the first embodiment of the present disclosure.

FIG. 4B is a diagram illustrating an example of a device log DBaccording to the first embodiment of the present disclosure.

FIG. 4C is a diagram illustrating an example of a device log DBaccording to the first embodiment of the present disclosure.

FIG. 5 is a diagram illustrating an example of a correlation conditionDB according to the first embodiment of the present disclosure.

FIG. 6 is a diagram illustrating an example of a correlation score DBaccording to the first embodiment of the present disclosure.

FIG. 7 is a block diagram schematically illustrating a functionalconfiguration of a server according to a second embodiment of thepresent disclosure.

FIG. 8 is a flowchart illustrating an example of a grouping processperformed on all devices according to the second embodiment of thepresent disclosure.

FIG. 9 is a flowchart illustrating an example of a grouping processperformed on an additional device according to the second embodiment ofthe present disclosure.

FIG. 10 is a diagram illustrating an example of a group DB according tothe second embodiment of the present disclosure.

FIG. 11 is a flowchart illustrating an example of a grouping processaccording to a third embodiment of the present disclosure.

FIG. 12 is a diagram illustrating an example of a device log DBaccording to the third embodiment of the present disclosure.

FIG. 13 is a block diagram illustrating a hardware configuration exampleof an information processing apparatus according to an embodiment of thepresent disclosure.

DESCRIPTION OF EMBODIMENT(S)

Hereinafter, (a) preferred embodiment(s) of the present disclosure willbe described in detail with reference to the appended drawings. In thisspecification and the appended drawings, structural elements that havesubstantially the same function and structure are denoted with the samereference numerals, and repeated explanation of these structuralelements is omitted.

Note that the description is given in the following order.

-   1. First Embodiment-   1-1. System configuration-   1-2. Functional configuration of server-   1-3. Workflow of process-   1-4. Example of data-   1-5. Specific example of correlation determination-   2. Second Embodiment-   3. Third Embodiment-   4. Hardware Configuration-   5. Supplement    1. First Embodiment    (1-1. System Configuration)

FIG. 1 is a diagram illustrating a schematic configuration of a systemaccording to a first embodiment of the present disclosure. Withreference to FIG. 1, a system 10 according to the embodiment includesdevices 100 and a server 200. As an example of the devices 100, FIG. 1illustrates a smartphone 100 a, a tablet 100 b, a laptop personalcomputer (PC) 100 c, a wearable device 100 d, a car-mounted device 100e, a television 100 f, a game console 100 g, an air conditioner 100 h, alight 100 i, and a kitchen appliance 100 j.

The example of the devices 100 is not limited thereto, and the devices100 may include various other devices. For example, the devices 100 mayinclude a mobile device other than the smartphone, tablet, and laptopPC. In addition to the illustrated eyewear, the devices 100 may includeother wearable devices such as a contact lens type terminal, a watchtype terminal, a bracelet type terminal, a ring type terminal, aheadset, a terminal attached to clothing, a terminal integrated intoclothing, a terminal attached to shoes, a terminal integrated intoshoes, and a necklace type terminal. Specifically, the car-mounteddevice 100 e may be a car navigation system or a rear seat entertainmentsystem. The devices 100 are not limited to the television, gameconsoles, air conditioner, or light. The devices 100 may include everykind of consumer electronics (CE) devices having communication functionsand information processing functions.

The devices 100 may include a device shared by many and unspecifiedusers. For example, the devices 100 may include a public display, aterminal device used for an order system in a restaurant or the like,and a vending machine. In addition, sometimes the mobile device or thecar-mounted device may be rented or mounted on a rental car, and used bymany and unspecified users.

In the above described environment where there are various types ofdevices, one user often uses two or more of the devices 100. Forexample, the user may search for information on a movie by using thesmartphone 100 a during watching the movie on the television 100 f.Alternatively, the user may ride on a vehicle equipped with thecar-mounted device 100 e while holding the tablet 100 b. Alternatively,the user may switch on/off the air conditioner 100 h, switch on/off thelight 100 i, or make a dish by using the kitchen appliance 100 j whilewearing the wearable device 100 d. Alternatively, the user may searchfor walkthroughs of a game by using the laptop PC 100 c while playingthe game on the game console 100 g.

In such cases, if it becomes possible to associate the two or more ofthe devices 100 with each other as devices 100 used by one user, suchassociation helps acquiring various long-term logs of the user and helpsselecting an optimum output device for providing the user withinformation, for example. However, such association of the devices 100is possible only in a limited case, and in most cases it is difficult toassociate the devices 100 with each other.

For example, when one user has logged in to services using the same useraccount via two or more of the devices 100, these devices 100 can beassociated with each other. However, the devices 100 not always use thesame user account. In addition, in a device 100 for using a service thatdoes not require login or a device 100 without a function for performinga login operation, the user does not perform login at all.

On the other hand, for example, two or more devices 100 can beassociated with each other in the case where these devices 100 identifyusers by analyzing images acquired by their cameras and the identifiedusers are the same parson. However, the devices 100 do not always havethe cameras, and the images of the user are not always acquired. Inaddition, the image analysis to identify a user requires high processingload, and it is difficult to enhance its accuracy.

On the other hand, for example, two or more devices 100 can beassociated with each other in the case where network addresses such asIP addresses used for communication between these devices 100 areacquired and the network addresses are the same. In such a way, it ispossible to associate devices 100 that are connected to each other viaWi-Fi or the like in a house, for example. However, a mobile device thatperforms communication via a mobile network such as a mobile telephonenetwork even in the house has a different network address from a CEdevice that performs communication via a local area network (LAN) and afixed line through a router. Therefore, it is difficult to associate themobile device with the CE device.

According to the first embodiment (to be described later) of the presentdisclosure, for example, it is possible to easily evaluate a relationbetween devices 100 that are used by the same user by associating thedevices 100 with each other in a way different from the above describedexamples.

(1-2. Functional Configuration of Server)

FIG. 2 is a block diagram schematically illustrating a functionalconfiguration of a server according to an embodiment of the presentdisclosure. With reference to FIG. 2, a server 200 includes a device logacquisition unit 210, a device log DB 220, a correlation determinationunit 230, a correlation condition DB 240, and a correlation score DB250.

The server 200 is realized by one or a plurality of server apparatuseson a network. Each of the server apparatuses is realized by a hardwareconfiguration of an information processing apparatus (to be describedlater). For example, the device log acquisition unit 210 is realized bya communication apparatus, the device log DB 220, the correlationcondition DB 240, and the correlation score DB 250 are realized bymemory or a storage, and the correlation determination unit 230 isrealized by a processor such as a CPU. In the case where the server 200is realized by the plurality of server apparatus, each of the serverapparatuses realizes one of the illustrated functional configurations.Alternatively, it is also possible that one functional configuration isdistributed to the plurality of server apparatus.

The device log acquisition unit 210 acquires device logs provided by thedevices 100 illustrated in FIG. 1. For example, the device log may be atime-series log including information on a position of each device andinformation on a state caused by behavior of a user related to eachdevice. For example, the device log acquisition unit 210 is transmittedfrom a device periodically or when a certain event occurs. In theembodiment, it is assumed that unique IDs (device ID and user ID) areassigned to the devices and users who use the devices. The logs acquiredby the device log acquisition unit 21 are stored in the device log DB220. A specific example of data stored in the device log DB 220 isdescribed later.

The correlation determination unit 230 determines a correlation betweenat least two devices that provide the device logs. The correlationdetermination unit 230 determines a correlation between devices on thebasis of preset conditions of states of the devices, and stores theresult in the correlation score DB 250. The preset conditions areregistered in the correlation condition DB 240. In the embodiment, thecorrelation condition DB 240 defines correlation conditions mainly interms of whether or not respective devices are used by a same user. Inthe embodiment, a plus score is calculated for a combination of devicesthat are highly possible to have been used by a same user, and a minusscore is calculated for a combination of devices that are highlypossible to have been used by different users. A specific example ofdata stored in the correlation condition DB 240 and the correlationscore DB 250 is described later.

(1-3. Workflow of Process)

FIG. 3 is a flowchart illustrating an example of a process performed bythe correlation determination unit according to the first embodiment ofthe present disclosure. With reference to FIG. 3, the correlationdetermination unit 230 first performs a loop process for eachcombination of devices on log data stored in the device log DB 220(S101). Here, the correlation determination unit 230 acquires logs ofthe devices in the combination (S103). The acquired log may include aseries of logs of each device that have been acquired in chronologicalorder. It is assumed that the log includes at least time and positionalinformation.

Next, the correlation determination unit 230 determines whether or notpositions of the devices are in proximity at any point in time inaccordance with the logs (S105), and calculates a correlation score inprocesses in subsequent Steps S107 to S109 only for a combination ofdevices that are in proximity at any point in time. Conversely, in theillustrated example, a correlation score is not calculated for acombination of devices that are not in proximity at every point in time.Such a combination of devices is treated as a combination for which itis impossible to determine whether or not devices are correlated(whether or not the devices have been used by a same user), for example.In addition, in this determination, it is only necessary that positionsof devices are in proximity at least at a certain time. The positionsmay be different at a time other than the certain time.

In the case where it has been determined that the positions are inproximity at any point in time in the step S105, the correlationdetermination unit 230 further determines whether or not a conditionindicated by logs of the devices is registered in the correlationcondition DB 240 (S107). A specific example of the determinationprocesses is described later in addition to a detailed example of thecorrelation condition DB 240. Here, in the case where it has beendetermined that the condition is not registered, a correlation score isnot calculated in a way similar to the case where the positions are notin proximity, and the combination of the devices is treated as acombination for which it is impossible to determine whether or notdevices are correlated (whether or not the devices have been used by asame user), for example.

In the case where it has been determined that the condition indicated bythe logs of the devices is registered in the correlation condition DB240 in the step S107, the correlation determination unit 230 updates thecorrelation score of the devices recorded in the correlation score DB250, on the basis of information associated with the registeredcondition (S109). Here, as described later, correlation scores accordingto the embodiment include a positive score indicating a high correlationbetween devices and a negative score indicating a low correlationbetween devices. Accordingly, in the illustrated example, thecorrelation determination unit 230 determines level of a correlation ofa combination of devices that are in proximity at least at any point intime, in accordance with the conditions registered in the correlationcondition DB 240. On the other hand, the correlation determination unit230 does not actively determine a correlation between devices other thanthe above described devices, that is, devices that are not in proximityat every point in time or devices that do not satisfy the conditionsregistered in the correlation condition DB 240.

(1-4. Example of Data)

FIG. 4A to FIG. 4C are each a diagram illustrating an example of thedevice log DB according to the first embodiment of the presentdisclosure. With reference to FIG. 4A to FIG. 4C, records in the devicelog DB 220 may include fields of time 220 a, device ID 220 b, devicetype 220 c, state 220 d, detailed state 220 e, and position 220 f. FIG.4A to FIG. 4C will be referred to again for describing correlationdetermination in an example of the correlation condition DB 240 (to bedescribed later).

The time 220 a is a timestamp indicating time when a log has beenacquired. The device ID 220 b is an ID for identifying a device that hasprovided the log. The device type 220 c is the type of the device thathas provided the log (the device type may be separately defined inassociation with the device ID).

The state 220 d is a state of the device indicated by the device ID 220b at the time 220 a. With reference to the illustrated example, thestate 220 d may indicate a function of the device such as “videoplayback”, “web browsing”, or the like. Alternatively, the state 220 dmay indicate that the device has been switched on/off, simply indicatethat the device has been operated, or indicate that the device hasmoved, for example.

The detailed state 220 e indicates details of the state indicated by thestate 220 d. For example, like the illustrated example, the detailedstate 220 e may indicate the title of the video in the case where thestate 220 d is a “video playback” state. In the case where the state 220d is a “web browsing” state, the detailed state 220 e may indicate thetitle of the web page.

The position 220 f is a position of the device indicated by the deviceID 220 b at the time 220 a. For example, the position 220 f may bepositional information acquired by each device providing positioningusing a global navigation satellite system (GNSS) such as the GPS.Alternatively, the position 220 f may be a position that the user hasinput in advance as a position where the device is fixedly installed (inthis case, positional information provided by each device is a fixedvalue).

FIG. 5 is a diagram illustrating an example of the correlation conditionDB according to the first embodiment of the present disclosure. Withreference to FIG. 5, records in the correlation condition DB 240 mayinclude fields of state (1) 240 a, detailed state (1) 204 b, state (2)240 c, detailed state (2) 240 d, temporal relationship 240 e, detailedcondition 240 f, and correlation 240 g.

The states 240 a and 240 c designates states of devices in thecombination of which a correlation is determined. For example, in thecase where positions of two devices are in proximity at any point intime and one of the devices in the state 240 a and the other of thedevices is in the state 240 c, it may be possible to determine acorrelation between these devices.

With regard to the state 240 a and 240 c of the respective devices, thedetailed states 240 b and 240 d designate information used indetermination of the detailed condition 240 f (to be described later).Therefore, depending on the detailed condition 240 f, sometimes at leastany of the detailed states 240 b and 240 d is not set. In addition, thedetailed state 240 b and 240 b are not necessarily in proximity to thedetailed state 220 e in the device log DB 220.

The temporal relationship 240 e indicates a temporal relationshipbetween the states 240 a and 240 c of the respective devices. Theillustrated example includes two types of the temporal relationship:“synchronization” and “switchover”. The “synchronization” indicates thatthe states 240 a and 240 c of the devices are caused at the same time inparallel. The “switchover” indicates that the states 240 a and 240 c ofthe devices are caused alternately. A specific example of the temporalrelationship will be described later.

The detailed condition 240 f indicates an additional determinationcondition in the case where the states 240 a and 240 c are caused in thedevices in the combination and the temporal relationship 240 e issatisfied. For example, in a record 240-1, the detailed condition 240 fis that, in the case where one of the devices is in the “video playback”state (state 240 a) and the other of the devices is in the “webbrowsing” state (state 240 c), the video title (detailed state 240 b) ofvideo played back in the “video playback” state corresponds to the webpage title (detailed state 240 d) of a web page browsed in the “webbrowsing state”.

The correlation 240 g indicates an estimated correlation between thedevices in the combination in the case where a condition indicated ineach record is satisfied. The illustrated example includes two types ofthe correlation: “YES” and “NO”. “YES” indicates that the devices arehighly possible to have been used by a same user, and a correlationbetween the device is high. “NO” indicates that the devices are highlypossible to have been used by different users, and a correlation betweenthe devices is low.

As described above, according to the embodiment, the correlationdetermination unit 230 estimates a correlation of devices in acombination indicated by the correlation 240 g in the case where thecondition defined in the correlation condition DB 240 is satisfied. Inthe case where the conditions are not satisfied, the correlationdetermination unit 230 does not estimate the correlation (according tothe above example, the correlation determination unit 230 determinesthat the correlation is neither YES or NO).

FIG. 6 is a diagram illustrating an example of the correlation score DBaccording to the first embodiment of the present disclosure. Withreference to FIG. 6, records in the correlation score DB 250 may includefields of device ID (1) 250 a, device ID (2) 250 b, and correlationscore 250 c.

According to the embodiment, the correlation determination unit 230represents a correlation between devices determined in the abovedescribed process, as a correlation score. For example, the correlationdetermination unit 230 may add a predetermined correlation score withregard to a combination of devices in the case where a condition definedin a certain record in the correlation condition DB 240 are satisfied,and the correlation 240 g of the certain record indicates that thecorrelation between the devices is high (“YES” in the example in FIG.5). On the other hand, in the case where the correlation 240 g indicatesthat the correlation between the devices is low (“NO” in the example inFIG. 5), the correlation determination unit 230 may subtract thepredetermined correlation score with regard to the combination of thedevices.

The device IDs 250 a and 250 b in the correlation score DB 250 of theillustrated example indicate devices in a combination for which acorrelation score have been calculated. The correlation score 250 cindicates a correlation score calculated for a combination of devices.For example, a record 250-1 indicates that a correlation score “+0.75”has been calculated for a combination of a device of ID “000001” and adevice of ID “000002”. Although this score is the positive correlationscore like a record 250-2, the sore of the record 250-1 is higher thanthe record 250-2.

For example, in the case where a combination of certain devicessatisfies a plurality of conditions defined in the correlation conditionDB 240 and all the conditions indicate that the correlation between thecertain devices is high, the correlation determination unit 230 mayintegrate correlation scores corresponding to the respective conditionswith regard to the certain devices. In this case, the correlation score250 c becomes higher as the combination of devices satisfies moreconditions (indicating that the correlation between the devices ishigh). Alternatively, the condition defined in the correlation conditionDB 240 does not have to correspond to a binary correlation (YES or NO)as illustrated in FIG. 5, but may correspond to a gradual correlationaccording to strength of the estimated correlation. According to such aconfiguration, it is possible to represent a gradual correlation of eachcombination of devices in the embodiment.

On the other hand, a record 250-3 in the correlation score DB 250 of theillustrated example indicates that a correlation score “−0.50” has beencalculated for a combination of the device of ID “000001” and a deviceof ID “000004”. As described above, the negative correlation score iscalculated in the case where the devices are highly possible to havebeen used by different users, and a correlation between the devices islow.

(1-5. Specific Example of Correlation Determination)

Next, with reference to the examples of a device log DB in FIG. 4A toFIG. 4C and the example of a correlation condition DB in FIG. 5, aspecific example of correlation determination) according to theembodiment will be described.

As a first example, an example of two device (TV and smartphone) will bedescribed with reference to FIG. 4A. Records 220-1 and 220-2 in thedevice log DB 220 illustrated in FIG. 4A indicate that the TV and thesmartphone have been in the same position (home) at the same time(2014/3/24 21:00:00). Therefore, the combination of the TV and thesmartphone in the records 220-1 and 220-2 goes through the determinationin S105 in FIG. 3 and proceeds to determination based on the conditiondefined in the correlation condition DB 240.

At the above described time, the TV is in the “video playback” state,and the smartphone is in the “web browsing” state. Therefore, the statesof these devices correspond to a “video playback” state 240 a and a “webbrowsing” state 240 c in the record 240-1 in the correlation conditionDB 240 illustrated in FIG. 5. In addition, since the states of thesedevices are simultaneously caused at the above described time, thetemporal relationship 240 e “synchronization” in the record 240-1 issatisfied.

Subsequently, in order to determine whether a detailed condition 240 f“titles correspond to each other” in the record 240-1 is satisfied, thecorrelation determination unit 230 acquires information recorded indetailed states 220 e in the records 220-1 and 220-2. In the illustratedexample, the detailed state 220 e in the record 220-1 indicates that thetitle of the video that is being played back is “XXXXX”. In addition,the detailed state 220 e in the record 220-2 indicates that the title ofthe web page that is being browsed is “XXXXX fan page”. For example, thecorrelation determination unit 230 checks whether character strings ofthe titles match with each other, and determines that the titlescorrespond to each other and the detailed condition 240 f in the record240-1 is satisfied.

As a result of the above described determination, as shown in thecorrelation 240 g in the record 240-1, the correlation determinationunit 230 estimates that it is highly possible that the TV and thesmartphone of the records 220-1 and 220-2 are used by the same user andthe correlation between the devices are high. The record 240-1 in thecorrelation condition DB 240 corresponds to the state in which the useris browsing information on the video by using a second device(smartphone here) while playing back the video on a first device (TVhere), for example.

As described above, the correlation determination unit 230 determinesthat the correlation between the first device and the second device ishigh in the case where the first device provides the content of thefirst type, the second device provides the content of the second typethat is different from the first type at the same time, and the contentprovided by these devices has a commonality. The combination of thetypes of the content is not limited to the video and the web page likethe above described example. Combinations of any kinds of content arepossible such as music content, a TV program, and an electronic book.

As a second example, an example of two device (TV and tablet) will bedescribed with reference to FIG. 4B. Records 220-3 and 220-5 in thedevice log DB 220 illustrated in FIG. 4B indicate that the TV and thetablet have been in the same position (home) at the same time (2014/3/2421:00:00). Therefore, the combination of the TV and the tablet in therecords 220-3 to 220-6 goes through the determination in S105 in FIG. 3and proceeds to determination based on the condition defined in thecorrelation condition DB 240.

The records 220-4 and 220-6 indicate that the devices are in differentpositions at another point in time (2014/3/24 21:30:00). However, asdescribed above, it is only necessary that the devices are in proximityat any point in time in the determination in S105, the devices can be atdifferent position at another point in time. Therefore, as describedabove, the combination of the TV and the tablet illustrated in FIG. 4Bgoes through the determination in S105 and proceeds to subsequentdetermination.

The record 220-3 indicates a state of the TV among the records 220-3 to220-6. The record 220-3 indicates that the TV has been playing back thevideo at 21:00. The record 220-4 indicates that a state of the TV hasnot been detected at 21:30 (it may be possible that the TV has beenpowered off). On the other hand, the record 220-6 indicates a state ofthe tablet. The record 220-6 indicates that the tablet has been playingback the video at 21:30. The record 220-5 indicates that a state of thetablet has not been detected at 21:00 (it may be possible that thetablet has not provided any functions).

Since the TV is in the “video playback” state and the tablet is also inthe “video playback” state in this case, it may be possible that thecombination of the TV and the tablet in the records 220-3 and 220-6satisfies a condition in a record 240-2 or 240-3 in the correlationcondition DB 240 illustrated in FIG. 5. The temporal relationship 240 ein the record 240-2 is “switchover”. The “switchover” indicates that thestates 240 a and 240 c of the devices are caused alternately. On theother hand, the temporal relationship 240 e in the record 240-3 is“synchronization”. The “video playback” state common in the records220-3 and 220-6 has been caused in the TV at 21:00 and caused in thetablet at 21:30. Therefore, the temporal relationship 240 e of“switchover” defined in the record 240-2 is satisfied.

Subsequently, in order to determine whether a detailed condition 240 f“titles correspond to each other” in the record 240-2 is satisfied, thecorrelation determination unit 230 acquires information recorded indetailed states 220 e in the records 220-3 and 220-6. In the illustratedexample, the detailed state 220 e in the record 220-3 indicates that thetitle of the video that has been played back is “XXXXX”. In addition,the detailed state 220 e in the record 220-6 indicates that the title ofthe video that has been played back is “XXXXX”. For example, thecorrelation determination unit 230 checks whether character strings ofthe titles match with each other, and determines that the titlescorrespond to each other and the detailed condition 240 f in the record240-2 is satisfied.

As a result of the above described determination, as shown in thecorrelation 240 g in the record 240-2, the correlation determinationunit 230 estimates that it is highly possible that the TV and the tabletof the records 220-3 and 220-6 have been used by the same user and thecorrelation between the devices is high. The record 240-2 in thecorrelation condition DB 240 corresponds to the state in which a userhas played back the video by using a first device (TV here), and theuser has gone from one room to another room and has changed the firstdevice to a second device (tablet here) to continue playback of thevideo, for example.

As described above, the correlation determination unit 230 determinesthat the correlation between the first device and the second device ishigh in the case where the first device provides the content of thefirst type, the second device provides the content of the first typealternately with the first device, and the content provided by thesedevices has a commonality. The types of the content are not limited tothe video like the above described example. Any kinds of content ispossible such as music content, a web page, a TV program, and anelectronic book.

As a third example, an example of two devices (TV and smartphone) willbe described with reference to FIG. 4C. Records 220-7 and 220-8 in thedevice log DB 220 illustrated in FIG. 4C indicate that the TV and thetablet have been in the same position (home) at the same time (2014/3/2421:00:00). Therefore, the combination of the TV and the smartphone inthe records 220-7 and 220-8 goes through the determination in S105 inFIG. 3 and proceeds to determination based on the condition defined inthe correlation condition DB 240.

At the above described time, the TV is in the “video playback” state,and the smartphone is also in the “video playback” state. Therefore, itmay be possible that the states of these devices correspond to acondition in a record 240-2 or the record 240-3 in the correlationcondition DB 240 illustrated in FIG. 5. In addition, since the states ofthese devices have been simultaneously caused at the above describedtime, the temporal relationship 240 e “switchover” in the record 240-2is not satisfied but the temporal relationship 240 e “synchronization”in the record 240-3 is satisfied.

In the record 240-3, the detailed condition 240 f is not set. In otherwords, unlike the two examples described above, the condition defined inthe record 240-3 is satisfied when the two devices (TV and smartphone)have been in the same position at the same time and are playing backvideo (titles of video do not matter). In this case, as shown in thecorrelation 240 g in the record 240-3, the correlation determinationunit 230 estimates that it is highly possible that the TV and thesmartphone of the records 220-7 and 220-8 have been used by differentusers and the correlation between the devices is low. The record 240-3in the correlation condition DB 240 corresponds to the state in which afirst device (TV here) and a second device (smartphone here) are used bydifferent users who are in proximity (for example, users live in thesame house) to play back video, for example.

As described above, the correlation determination unit 230 determinesthat the correlation between the first device and the second device islow in the case where the first device provides the content of the firsttype, and the second device also provides content of the first type atthe same time. The combination of the types of the content is notlimited to the video and the web page like the above described example.Combinations of any kinds of content are possible such as music content,a TV program, and an electronic book.

Next, with reference to the other examples of the correlation conditionDB illustrated in FIG. 5, the specific example of correlation estimationwill be described.

In a way similar to the record 240-3 in the third example describedabove, the record 240-4 in the correlation condition DB 240 estimatesthat it is highly possible that two devices are used by different usersand the correlation between the two devices is low in the case where thetwo devices are playing back music in synchronization. For example, therecord 240-3 corresponds to a state in which a first device and a seconddevice are used by different users who are in proximity to play backmusic. In a way similar to the records 240-3 and 240-4, the conditionthat the correlation between two devices is estimated to be low in thecase where the two devices are playing back music in synchronization maybe set with regard to content other than the video and the music.

A record 240-5 in the correlation condition DB 240 defines a conditionthat a correlation between two devices is estimated to be low ifmovement trajectories detected by the two devices are different in thecase where the two devices detect movement of users at the same time bycontinuously acquiring positional information, for example. The devicesmoving along different movement trajectories at the same time are highlypossible to have been used by different users.

A record 240-6 in the correlation condition DB 240 defines a conditionthat a correlation between two devices is estimated to be high if thetitle of a game that is being played with a first device corresponds tothe title of a web page that is being browsed with a second device inthe case where the first device provides a game function and the seconddevice provides a web browsing function at the same time. In this case,for example, it is estimated that a user is playing the game with thefirst device (for example, game console) while referring to walkthroughsof the game with the second device (for example, tablet).

A record 240-7 in the correlation condition DB 240 defines a conditionthat a correlation between devices is estimated to be low if a seconddevice (in any state) is detecting some user operation in the case wherea first device is detecting that a state (behavior) of the user is asleep state. When the devices are used by the same user, one device doesnot detect the user operation while the other device is detecting thesleep state.

A record 240-8 in the correlation condition DB 240 defines a conditionthat a correlation between two devices is estimated to be high when asecond device (that is carried by a user and moves along with the user)finishes moving and an end point of the movement of the second device isin proximity to the installation site of a first device that has beenfixedly installed in the case where the first device is turned on. Forexample, when a light or an air conditioner is turned on, a devicemoving toward the installation site of the light or the air conditioneris highly possible to have been carried or worn by a user arrived at theinstallation site (house, office, or the like).

A record 240-9 in the correlation condition DB 240 defines a conditionthat a correlation between two devices is estimated to be high when asecond device (that is carried by a user and moves along with the user)starts moving and a starting point of the movement of the second deviceis in proximity to the installation site of a first device that has beenfixedly installed in the case where the first device is turned off. Forexample, when a light or an air conditioner is turned off, a devicestarting to move away from the installation site of the light or the airconditioner is highly possible to have been carried or worn by a userleft from the installation site (house, office, or the like).

A record 240-10 in the correlation condition DB 240 defines a conditionthat a correlation between devices is estimated to be high when a seconddevice detects that a state (behavior) of a user is a wake-up state inthe case where a first device is turned on. For example, if a user wakesup when the light or the air conditioner is turned on, it is highlypossible that the light or the air conditioner in his/her home has beenturned on by the user as behavior after waking up.

A record 240-11 in the correlation condition DB 240 defines a conditionthat a correlation between devices is estimated to be high when a seconddevice detects that a user goes to bed as a state (behavior) of the userin the case where a first device is turned on. For example, if a usergoes to bed when the light or the air conditioner is turned off, it ishighly possible that the light or the air conditioner in his/her homehas been turned off by the user as behavior before going to bed.

A record 240-12 in the correlation condition DB 240 defines a conditionthat a correlation between two devices is estimated to be high when adevice (second device) is providing the web browsing function and arecipe in a web page corresponds to a type of operation of a kitchenappliance (first device), in the case where the operation on the kitchenappliance is detected. This condition also indicates that the seconddevice provides an instruction (recipe) about user operation of apredetermined pattern in the case where the user operation of thepredetermined pattern on the first device is detected. For example, itis highly possible that a smartphone and a microwave oven are used bythe same user when the microwave oven starts heating for three minuteswhile the smartphone is displaying a web page of a recipe including astep “heating it in a microwave for three minutes”.

A record 240-13 in the correlation condition DB 240 defines a conditionthat a correlation between devices is estimated to be high when adestination of navigation provided by a second device is in proximity toan end point of a moving trajectory in the case where a first device isdetecting movement of a vehicle. For example, this condition issatisfied in the case where the first device is a car-mounted device,the second device is a smartphone, and a user uses the navigation in thesmartphone while riding a car equipped with the car-mounted device.

A record 240-14 in the correlation condition DB 240 defines a conditionthat a correlation between devices is estimated to be high when a stateof a user detected by a second device is a sitting state and duration ofthe sitting corresponds to duration of movement of a vehicle in the casewhere a first device detects the movement. In this case, sitting postureis posture of the user on a vehicle. According to the type or the stateof the vehicle, standing posture or other posture may be detected. Forexample, a mobile device detects a sitting state of a user when a firstdevice is a car-mounted device, a second device is the mobile device,and the user is traveling while sitting in a car equipped with thecar-mounted device.

As described above, in the embodiment, the correlation condition DB 240defines the conditions that a correlation between devices is estimatedto be high or low. Therefore, a correlation between devices cannot bedetermined be high or low when the devices do not satisfy any conditiondefined in the correlation condition DB 240. The correlationdetermination unit 230 does not estimate a correlation between suchdevices and leaves it as it is. For example, the correlation cannot bedetermined to be high or low in the case where a first device detectsthat a state (behavior) of a user is an exercise state and a seconddevice (in any state) detects user operation. This is because sometimesthe user operates the device while exercising. Since the correlation insuch a case is treated as “unknown” according to the embodiment, it ispossible to prevent erroneous estimation and therefore it is possible toimprove reliability of the estimation result.

In addition to the above described examples, there are various examplesof the condition for estimating a correlation between devices. Forexample, a correlation between a public display and a mobile device isestimated to be high in the case where environment conditions such astemperature, humidity, brightness, and sound are satisfied, and themobile device has detected that the user is in a stop state. Informationindicating the environment conditions such as temperature, humidity,brightness, and sound is information indicating states caused bybehavior of a user related to each device since the environmentconditions change when the user goes to a specific place.

For example, when a correlation between a mobile device and a terminaldevice used for an order system in an restaurant or the like isestimated, the correlation between the mobile device and the terminaldevice is estimated to be high in the case where a picture of a menuordered through the terminal device is posted on social media via themobile device.

(2. Second Embodiment)

Next, a second embodiment of the present disclosure will be described. Aschematic configuration of a system according to the embodiment issimilar to the system 10 described with reference to FIG. 1. Therefore,repeated description is omitted.

FIG. 7 is a block diagram schematically illustrating a functionalconfiguration of a server according to the second embodiment of thepresent disclosure. With reference to FIG. 7, a server 400 includes thedevice log acquisition unit 210, the device log DB 220, the correlationdetermination unit 230, the correlation condition DB 240, thecorrelation score DB 250, a grouping unit 410, an area DB 420, and agroup DB 430.

In a way similar to the server 200 according to the first embodiment,the server 400 is realized by one or a plurality of server apparatuseson a network. Each of the server apparatuses is realized by a hardwareconfiguration of an information processing apparatus (to be describedlater). For example, the device log acquisition unit 210 is realized bya communication apparatus, the device log DB 220, the correlationcondition DB 240, the correlation score DB 250, the area DB 420, and thegroup DB 430 are realized by memory or a storage, and the correlationdetermination unit 230 and the grouping unit 410 are realized by aprocessor such as a CPU. In the case where the server 400 is realized bythe plurality of server apparatuses, each of the server apparatusesrealizes one of the illustrated functional configurations.Alternatively, it is also possible that one functional configuration isdistributed to the plurality of server apparatus.

Next, the functional configuration of the server 400 will be described.With regard to the device log acquisition unit 210, the device log DB220, the correlation determination unit 230, the correlation conditionDB 240, the correlation score DB 250, repeated description similar tothe first embodiment is omitted.

The grouping unit 410 performs grouping on the devices 100 illustratedin FIG. 1 on the basis of data stored in the device log DB 220. Morespecifically, the grouping unit 410 performs grouping on the devices 100on the basis of a relation between positions of the devices 100indicated by logs stored in the device log DB 220, and a geographicalarea defined in the area DB 420 (positional conditions set in advance).For example, the area DB 420 defines areas corresponding to addressareas. In this case, the grouping unit 410 converts positions of thedevices 100 indicated by the logs or the like to addresses by using anexternal service or the like, and performs grouping on the devices 100on the basis of the addresses. As the conversion from the positionalinformation to the addresses, known technologies may be used such as JP2008-89815A and JP 2011-43626A. The grouping unit 410 stores a result ofthe grouping in the group DB 430.

As described above, in the case where the devices 100 are subjected tothe grouping process on the basis of the predefined areas, the groupingunit 410 performs grouping on the devices 100 under a condition thatpositions of the devices are in a common area at any point of time. Thedevices 100 that are in the common area at any point of time may beclassified into the same group. In such a way, the grouping unit 410classifies the devices into groups depending on areas.

In the embodiment, the correlation determination unit 230 determines acorrelation between at least two devices that provide the device logs ina way similar to the first embodiment. However, in the server 400according to the second embodiment, the correlation determination unit230 refers to the group DB 430 and determines the correlation betweenthe devices in the group. As described above, the group into which thedevices are classified corresponds to the geographical area defined inthe area DB 420, for example, the address area. The correlationdetermination unit 230 narrows down analysis targets to the devices insuch a geographical area. Therefore, it is possible to reduce acalculation amount in comparison with the case where all the devices aretreated as the analysis targets.

For example, in the case where the number of devices serving as theanalysis targets is N and a relation between devices are determined foreach combination of devices, calculation has to be performed N² timesfor all the combinations. Quadratic functional increase in the number oftimes of the calculation occurs as N increases. Therefore, in respect ofreduction in the calculation amount, it is effective to limit theanalysis targets to the devices in the same geographical area asdescribed above.

FIG. 8 is a flowchart illustrating an example of a grouping processperformed on all devices according to the second embodiment of thepresent disclosure. With reference to FIG. 8, the grouping unit 410performs a loop process for each area defined in the area DB 420 (S201),and groups devices on an area basis (S203). More specifically, forexample, the grouping unit 410 groups the devices by checking whether anaddress area converted from the positional information of each devicestored in the device log DB 220 matches with the address areas definedin the area DB 420.

A device in different positions according to time such as the mobiledevice may be included in a plurality of device groups of a plurality ofareas. In this case, the positional information used for grouping thedevices may be limited to information on a position where stay over apredetermined time has been detected.

The grouping unit 410 performs the grouping process on all the devicesas an initial process or as batch processing performed periodically.

FIG. 9 is a flowchart illustrating an example of a grouping processperformed on an additional device according to the second embodiment ofthe present disclosure. With reference to FIG. 9, the grouping unit 410performs a loop process for each device of which the device logacquisition unit 210 has newly acquired a log, in other words, for eachdevice that has been newly added, for example (S205). Here, the groupingunit 410 determines whether or not the new device is included in theareas of the existing groups (S207). More specifically, for example, thegrouping unit 410 checks whether an address area converted from thepositional information of the new device matches with the address areascorresponding to the groups recorded in the group DB 430.

In the case where it has been determined that the new device is includedin any of the areas of the existing groups (YES in S207), the groupingunit 410 adds the new device in the existing group (S209). On the otherhand, in the case where the new device is not included in any of theareas of the existing groups (NO), the new device does not belong to anygroup in the illustrated example. In another example, the grouping unit410 may additionally create a group including the new device in the casewhere the new device is not included in any of the areas of the existinggroups.

The grouping unit 410 may perform the grouping process on the additionaldevice periodically or every time a device is added after the groupingprocess illustrated in FIG. 9 is performed on all the devices at leastonce.

FIG. 10 is a diagram illustrating an example of the group DB accordingto the second embodiment of the present disclosure. With reference toFIG. 10, records in the group DB 430 may include fields of group ID 430a, group detail 430 b, and device ID 430 c.

The group ID 430 a is an ID for identifying a group. The group ID 430 amay be associated with an ID for defining an area in the area DB 420.The group detail 430 b describes detailed information of a group. In theillustrated example, the group detail 430 b describes an address of anaddress area corresponding to a group. The group detail 430 b is notnecessary in the case where there is other information associating thegroup ID with the area defined in the area DB 420. For example, thegroup detail 430 b that describes the address may be used for notifyinga user of a group to which a device belongs. The device ID 430 cindicates the device that belongs to the group.

The means for recording a group to which each device belongs is notlimited to the group DB 430 in the above example. For example, thedevice log DB 220 may record a group to which each device belongs at atime of acquiring a log. Alternatively, for example, the area DB 420 mayrecord a device that belongs to a group corresponding to each area.Alternatively, a device DB may be provided in addition to the device logDB 220 to record a group to which each device belongs.

(3. Third Embodiment)

Next, a third embodiment of the present disclosure will be described. Inthe third embodiment, additional grouping is performed on a mobiledevice in addition to the grouping process according to the secondembodiment. Configuration other than the additional grouping in thethird embodiment is similar to the second embodiment. Therefore,repeated description will be omitted.

FIG. 11 is a flowchart illustrating an example of a grouping processaccording to the third embodiment of the present disclosure. Withreference to FIG. 11, the grouping unit 410 performs a loop process foreach group based on the area generated by a grouping process after thegrouping process like the second embodiment is performed on all thedevices at least once (S301). Here, the grouping unit 410 extractsmobile devices in each group and performs the loop process for each ofthe mobile devices (S303). In addition, the grouping unit 410 performsthe loop process with the other device in the group for each of themobile devices (S305).

In the loop process, the grouping unit 410 determines whether or not theother device is operated when proximity of the other device to themobile device is detected (S307). More specifically, for example, thegrouping unit 410 specifies time when the mobile device has come closerto the other device on the basis of the device log, and determineswhether or not a user operation performed on the other device at thattime is recorded in the device log. In the case where the other deviceis operated when the mobile device comes closer to the other device(YES), the grouping unit 410 adds the mobile device and the other devicein a group based on an operation history (S309). The grouping unit 410stores information on the group based on the operation history in thegroup DB 430.

According to the third embodiment, the group based on the operationhistory may be treated similar to the group based on the area accordingto the second embodiment. In other words, when determining a correlationbetween devices, the correlation determination unit 230 refers to thegroup DB 430 and determines the correlation between the devices in thegroup based on the operation history. The group based on the operationhistory may be coexistent with the group based on the area. According tothe third embodiment, devices included in groups based on areas arefurther classified into subgroups based on operation histories accordingto a relation with each mobile device in the groups (sometimes onedevice belongs to a plurality of groups based on the operationhistories). In the embodiment, a target of determination of acorrelation between devices other than mobile devices may also bedevices in a group based on an area, for example.

In the case where a correlation between the mobile device and the otherdevices is determined in terms of whether or not the mobile device andthe other devices are used by the same user, it is unlikely that adevice that is not operated when the device is in proximity to themobile device that has been estimated to be carried by the user has ahigh correlation with the mobile device. Therefore, when determining thecorrelation with the mobile device, it is reasonable to limit ananalysis area to a group based on the operation history. In theembodiment, the devices in the group based on the area are furtherclassified into groups based on the operation histories. Thereby, it ispossible to break down the group and reduce a calculation amount whilemaintaining an accuracy of determination.

FIG. 12 is a diagram illustrating an example of the device log DBaccording to the third embodiment of the present disclosure. Withreference to FIG. 12, the device log DB 220 includes a record 220-9 of asmartphone, a record 220-10 of a TV, and a record 220-11 of an airconditioner.

The record 220-9 and record 220-10 indicate that the smartphone and theTV have been in proximity at a certain point of time (2014/1/1 10:00)(the smartphone and the TV are in home), and the TV has been operated(turned on) at that time. In this case, the grouping unit 410 accordingto the embodiment may perform a process to classify the smartphone(mobile device) in the record 220-9 and the TV (another device) in therecord 220-10 into the same group on an operation history basis.

On the other hand, the record 220-9 and the record 220-11 indicate thatalthough the smart phone and the air conditioner have been in proximity(the air conditioner has been estimated to be in the home at 2014/1/110:00 since the air conditioner has been fixedly installed), the airconditioner has not been operated at that time and the air conditionerhas been operated (turned on) after the smartphone has gotten away fromthe air conditioner. In this case, the grouping unit 410 according tothe embodiment does not perform a process to classify the smartphone(mobile device) in the record 220-9 and the air conditioner (anotherdevice) in the record 220-11 into the same group on an operation historybasis.

(Modification)

In the above described embodiments, the devices that has classified intogroups on an area basis are further subjected to grouping on anoperation history basis. However, in another example, all devices may besubjected to grouping on an operation history basis before the devicesare classified into groups on the area basis. In this case, the devicesare first subjected to grouping based on a relation with each mobiledevice. For example, in the case where a TV in a home and a PC in anoffice are operated when a common mobile device comes closer to the TVor the PC, the TV and the PC may be classified into a same group. Inaddition, as necessary, the group on an operation history basis may bebroken down into subgroups on an area basis.

According to the first to third embodiments of the present disclosure,it is possible to estimate a correlation between various devices such asa mobile device, a wearable device, a car-mounted device, and a CEdevice. In addition, by organizing the devices according to theircorrelations, it is possible to acquire more detailed profile ofpreference and behavior patterns of users, and it is possible to selectan optimum device to present information to a user.

In addition, for example, when a correlation between a device shared bymany and unspecified users such as a public display and a devicededicated to an individual user such as a mobile device is estimated, itis possible to individually track effects of information (for example,advertisement) provided by the shared device on behavior of the user. Inaddition, for example, information output via the shared device may bechanged according to the user in the case where the shared device istemporally occupied by the user or in the case where it is estimatedthat a rate of the number of users having a specific attribute among theusers using the shared device is high.

In the first to third embodiments, the correlation is estimated in termsof whether or not the devices are used by the same user. However, theembodiments of the present disclosure are not limited thereto. Forexample, the correlation between the devices may be estimated in termsof whether or not the devices are used by users having the sameattribute such as age, job, or sex. In addition, the grouping processaccording to the second and third embodiments is not limited to the caseof determining a correlation between devices. The grouping process maybe useful for reducing a calculation amount also in any case ofanalyzing a correlation of a combination of certain devices in a devicegroup. In this respect, it can be said that the correlationdetermination unit 230 according to the above described embodiments is arelation analysis unit that analyzes a relation between devices on thebasis of device logs.

(4. Hardware Configuration)

Next, with reference to FIG. 13, a hardware configuration of aninformation processing apparatus according to an embodiment of thepresent disclosure is explained. FIG. 13 is a block diagram illustratinga hardware configuration example of an information processing apparatusaccording to the embodiment of the present disclosure. An illustratedinformation processing apparatus 900 may achieve the server apparatusaccording to the embodiments of the present disclosure, for example.

The information processing apparatus 900 includes a central processingunit (CPU) 901, read only memory (ROM) 903, and random access memory(RAM) 905. In addition, the information processing apparatus 900 mayinclude a host bus 907, a bridge 909, an external bus 911, an interface913, an input apparatus 915, an output apparatus 917, a storageapparatus 919, a drive 921, a connection port 923, and a communicationapparatus 925. Moreover, the information processing apparatus 900 mayinclude an imaging apparatus 933, and a sensor 935, as necessary. Theinformation processing apparatus 900 may include a processing circuitsuch as a digital signal processor (DSP), an application-specificintegrated circuit (ASIC), or a field-programmable gate array (FPGA),alternatively or in addition to the CPU 901.

The CPU 901 serves as an arithmetic processing apparatus and a controlapparatus, and controls the overall operation or a part of the operationof the information processing apparatus 900 according to variousprograms recorded in the ROM 903, the RAM 905, the storage apparatus919, or a removable recording medium 927. The ROM 903 stores programs,operation parameters, and the like used by the CPU 901. The RAM 905transiently stores programs used when the CPU 901 is executed, andvarious parameters that change as appropriate when executing suchprograms. The CPU 901, the ROM 903, and the RAM 905 are connected witheach other via the host bus 907 configured from an internal bus such asa CPU bus or the like. The host bus 907 is connected to the external bus911 such as a Peripheral Component Interconnect/Interface (PCI) bus viathe bridge 909.

The input apparatus 915 is a device operated by a user such as a mouse,a keyboard, a touch panel, a button, a switch, and a lever. The inputapparatus 915 may be a remote control device that uses, for example,infrared radiation and another type of radiowave. Alternatively, theinput apparatus 915 may be an external connection apparatus 929 such asa mobile phone that corresponds to an operation of the informationprocessing apparatus 900. The input apparatus 915 includes an inputcontrol circuit that generates input signals on the basis of informationwhich is input by a user to output the generated input signals to theCPU 901. A user inputs various types of data to the informationprocessing apparatus 900 and instructs the information processingapparatus 900 to perform a processing operation by operating the inputapparatus 915.

The output apparatus 917 includes an apparatus that can report acquiredinformation to a user visually, audibly, or haptically. The outputapparatus 917 may be, for example, a display device such as a liquidcrystal display (LCD) or an organic electro-luminescence (EL) display,an audio output apparatus such as a speaker or a headphone, or avibrator. The output apparatus 917 outputs a result obtained through aprocess performed by the information processing apparatus 900, in theform of video such as text and an image, sounds such as voice and audiosounds, or vibration.

The storage apparatus 919 is an apparatus for data storage that is anexample of a storage unit of the information processing apparatus 900.The storage apparatus 919 includes, for example, a magnetic storagedevice such as a hard disk drive (HDD), a semiconductor storage device,an optical storage device, or a magneto-optical storage device. Thestorage apparatus 919 stores therein the programs and various dataexecuted by the CPU 901, various data acquired from an outside, and thelike.

The drive 921 is a reader/writer for the removable recording medium 927such as a magnetic disk, an optical disc, a magneto-optical disk, and asemiconductor memory, and built in or externally attached to theinformation processing apparatus 900. The drive 921 reads outinformation recorded on the mounted removable recording medium 927, andoutputs the information to the RAM 905. The drive 921 writes the recordinto the mounted removable recording medium 927.

The connection port 923 is a port used to connect devices to theinformation processing apparatus 900. The connection port 923 mayinclude a Universal Serial Bus (USB) port, an IEEE1394 port, and a SmallComputer System Interface (SCSI) port. The connection port 923 mayfurther include an RS-232C port, an optical audio terminal, aHigh-Definition Multimedia Interface (HDMI) (registered trademark) port,and so on. The connection of the external connection device 929 to theconnection port 923 makes it possible to exchange various data betweenthe information processing apparatus 900 and the external connectiondevice 929.

The communication apparatus 925 is a communication interface including,for example, a communication device for connection to a communicationnetwork 931. The communication apparatus 925 may be, for example, acommunication card for a local area network (LAN), Bluetooth (registeredtrademark), Wi-Fi, or a wireless USB (WUSB). The communication apparatus925 may also be, for example, a router for optical communication, arouter for asymmetric digital subscriber line (ADSL), or a modem forvarious types of communication. For example, the communication apparatus925 transmits and receives signals in the Internet or transits signalsto and receives signals from another communication device by using apredetermined protocol such as TCP/IP. The communication network 931 towhich the communication apparatus 925 connects is a network establishedthrough wired or wireless connection. The communication network 931 mayinclude, for example, the Internet, a home LAN, infrared communication,radio communication, or satellite communication.

The imaging apparatus 933 is an apparatus that captures an image of areal space by using an image sensor such as a charge coupled device(CCD) and a complementary metal oxide semiconductor (CMOS), and variousmembers such as a lens for controlling image formation of a subjectimage onto the image sensor, and generates the captured image. Theimaging apparatus 933 may capture a still image or a moving image.

The sensor 935 is various sensors such as an acceleration sensor, anangular velocity sensor, a geomagnetic sensor, an illuminance sensor, atemperature sensor, a barometric sensor, and a sound sensor(microphone). The sensor 935 acquires information regarding a state ofthe information processing apparatus 900 such as a posture of a housingof the information processing apparatus 900, and information regardingan environment surrounding the information processing apparatus 900 suchas luminous intensity and noise around the information processingapparatus 900. The sensor 935 may include a global positioning system(GPS) receiver that receives GPS signals to measure latitude, longitude,and altitude of the apparatus.

The example of the hardware configuration of the information processingapparatus 900 has been described. Each of the structural elementsdescribed above may be configured by using a general purpose componentor may be configured by hardware specialized for the function of each ofthe structural elements. The configuration may be changed as necessaryin accordance with the state of the art at the time of working of thepresent disclosure.

(5. Supplement)

The embodiments of the present disclosure may include, for example, theabove-described information processing apparatus (for example, server),the above-described system, the information processing method executedby the information processing apparatus or the system, a program forcausing the information processing apparatus to exhibits its function,and a non-transitory physical medium having the program stored therein.

The preferred embodiment(s) of the present disclosure has/have beendescribed above with reference to the accompanying drawings, whilst thepresent disclosure is not limited to the above examples. A personskilled in the art may find various alterations and modifications withinthe scope of the appended claims, and it should be understood that theywill naturally come under the technical scope of the present disclosure.

Further, the effects described in this specification are merelyillustrative or exemplified effects, and are not limitative. That is,with or in the place of the above effects, the technology according tothe present disclosure may achieve other effects that are clear to thoseskilled in the art based on the description of this specification.

Additionally, the present technology may also be configured as below.

-   (1)

An information processing apparatus including:

a device log acquisition unit configured to acquire a time-series devicelog including information indicating a position of each of devices fromeach of the devices;

a grouping unit configured to classify the devices into at least onegroup on the basis of the device logs and a preset condition of thepositions; and

a relation analysis unit configured to analyze a relation between thedevices in each of the at least one group on the basis of the devicelogs.

-   (2)

The information processing apparatus according to (1), wherein

the condition includes a first condition that the positions of therespective devices are included in a common area at least at any pointin time, and

the grouping unit classifies the devices into the at least one groupbased on the areas in accordance with the first condition.

-   (3)

The information processing apparatus according to (2), wherein the areascorrespond to address areas.

-   (4)

The information processing apparatus according to (1), wherein

the devices include a mobile device and another device,

the condition includes a second condition that the another device isoperated when the mobile device and the another device are in proximity,and

the grouping unit classifies the mobile device and the another deviceinto a same group in a case where the second condition is satisfied.

-   (5)

The information processing apparatus according to (1), wherein

the devices include a mobile device and another device,

the condition includes a first condition that the positions of therespective devices are included in a common area at least at any pointin time, and a second condition that the another device is operated whenthe mobile device and the another device are in proximity, and

the grouping unit classifies the devices into a first group based on theareas in accordance with the first condition, and further classifies themobile device and the another device in the first group into at leastone subgroup in accordance with the second condition.

-   (6)

The information processing apparatus according to (1), wherein

the devices include a mobile device and another device,

the condition includes a first condition that the positions of therespective devices are included in a common area at least at any pointin time, and a second condition that the another device is operated whenthe mobile device and the another device are in proximity, and

the grouping unit classifies the mobile device and the another deviceinto a first group in accordance with the second condition, and furtherclassifies the devices in the first group into at least one subgroupbased on the areas in accordance with the first condition.

-   (7)

The information processing apparatus according to any one of (1) to (6),wherein

the device log further includes information indicating a state caused bybehavior of a user related to each of the devices, and

the relation analysis unit determines a correlation between the devicesin each of the at least one group on the basis of the device logs and apreset condition of the states.

-   (8)

An information processing method including:

acquiring a time-series device log including information indicating aposition of each of devices from each of the devices;

classifying, by a processor, the devices into at least one group on thebasis of the device logs and a preset condition of the positions; and

analyzing a relation between the devices in each of the at least onegroup on the basis of the device logs.

-   (9)

A program for causing a computer to achieve:

a function of classifying devices into at least one group on the basisof a preset condition of a position of each of the devices and atime-series device log that has been acquired from each of the devicesand that includes information indicating the position of each of thedevices; and

a function of analyzing a relation between the devices in each of the atleast one group on the basis of the device logs.

REFERENCE SIGNS LIST

-   10 system-   100 device-   200, 400 server-   210 device log acquisition unit-   220 device log DB-   230 correlation determination unit-   240 correlation condition DB-   250 correlation score DB-   410 grouping unit-   420 area DB-   430 group DB

The invention claimed is:
 1. An information processing apparatus,comprising: a circuitry configured to: acquire a time-series device logincluding position information indicating a position of each device of aplurality of devices, wherein the time-series device log is acquiredfrom each device of the plurality of devices; classify the plurality ofdevices into at least one group based on the time-series device log anda set condition associated with the position information; and determinea relation between at least two devices of the plurality of devices inthe at least one group based on the time-series device log, wherein therelation between the at least two devices is determined based on adetermination that the at least two devices of the plurality of devicesare used by a first user.
 2. The information processing apparatusaccording to claim 1, wherein the set condition includes a firstcondition that a plurality of positions of the plurality of devices areincluded in a common area at a point in time, and the classification ofthe plurality of devices into the at least one group is based on theplurality of positions associated with the first condition.
 3. Theinformation processing apparatus according to claim 2, wherein theplurality of positions correspond to address areas.
 4. The informationprocessing apparatus according to claim 1, wherein the plurality ofdevices include a mobile device and a consumer device, the set conditionincludes a second condition that operation of the consumer device isbased on proximity of the mobile device to the consumer device, and thecircuitry is further configured to classify the mobile device and theconsumer device into a same group based on satisfaction of the secondcondition.
 5. The information processing apparatus according to claim 1,wherein the plurality of devices include a mobile device and a consumerdevice, the set condition includes: a first condition that a pluralityof positions of the plurality of devices are included in a common areaat a point in time, and a second condition that operation of theconsumer device is based on proximity of the mobile device to theconsumer device, and the circuitry is further configured to: classifythe plurality of devices into a first group based on the plurality ofpositions associated with the first condition; and classify the mobiledevice and the consumer device in the first group into at least onesubgroup based on the second condition.
 6. The information processingapparatus according to claim 1, wherein the plurality of devices includea mobile device and a consumer device, the set condition includes: afirst condition that a plurality of positions of the plurality ofdevices are included in a common area at a point in time, and a secondcondition that operation of the consumer device is based on proximity ofthe mobile device to the consumer device, and the circuitry is furtherconfigured to: classify the mobile device and the consumer device into afirst group based on the second condition; and classify the plurality ofdevices in the first group into at least one subgroup based on theplurality of positions associated with the first condition.
 7. Theinformation processing apparatus according to claim 1, wherein thetime-series device log is further associated with state information ofthe plurality of devices, wherein the state information indicates astate caused by a behavior of the first user related to each of theplurality of devices, and the circuitry is further configured todetermine a correlation between the plurality of devices in the at leastone group based on the time-series device log and a set conditionassociated with the state information.
 8. The information processingapparatus according to claim 1, wherein the relation between the atleast two devices of the plurality of devices is determined as apositive score based on the determination that the at least two devicesof the plurality of devices are used by the first user.
 9. Theinformation processing apparatus according to claim 1, wherein therelation between the at least two devices of the plurality of devices isdetermined as a negative score based on the determination that the atleast two devices of the plurality of devices are used by a second userother than the first user.
 10. An information processing method,comprising: acquiring a time-series device log including positioninformation indicating a position of each device of a plurality ofdevices, wherein the time-series device log is acquired from each deviceof the plurality of devices; classifying, by a processor, the pluralityof devices into at least one group based on the time-series device logand a set condition associated with the position information; anddetermining a relation between at least two devices of the plurality ofdevices in the at least one group based on the time-series device log,wherein the relation between the at least two devices of the pluralityof devices is determined based on a determination that the at least twodevices of the plurality of devices are used by a first user.
 11. Anon-transitory computer-readable medium having stored thereon,computer-executable instructions, which when executed by a processor ofan information processing apparatus, cause the information processingapparatus to execute operations, the operations comprising: classifyinga plurality of devices into at least one group based on a set conditionassociated with a position of each device of the plurality of devicesand a time-series device log, wherein the time-series device log isacquired from each device of the plurality of devices, and wherein thetime-series device log is associated with position informationindicating the position of each device of the plurality of devices; anddetermining a relation between at least two devices of the plurality ofdevices in the at least one group based on the time-series device log,wherein the relation between the at least two devices of the pluralityof devices is determined based on a determination that the at least twodevices of the plurality of devices are used by a first user.